The field of legal information retrieval is moving towards more sophisticated and accurate methods of retrieving and predicting legal judgments and precedents. This is being driven by the development of new frameworks and models that incorporate structured legal knowledge, semantic search, and metadata filtering. The use of large language models and retrieval-augmented generation is also becoming more prevalent, allowing for more precise and meaningful retrieval of legal information. Notably, the integration of legal questions within the retrieval process is refining the context of queries and delivering more contextually relevant results.

Some noteworthy papers in this area include: NyayaRAG, which proposes a Retrieval-Augmented Generation framework that simulates realistic courtroom scenarios and evaluates the effectiveness of combined inputs in predicting court decisions. Augmented Question-guided Retrieval of Indian Case Law with LLM, RAG, and Structured Summaries, which introduces a novel approach to case law retrieval that generates targeted legal questions based on factual scenarios to identify relevant case law more effectively.